Suppose that the data mining task is to cluster the following eight points (with (x, y) representing location) into three clusters: (3, 10), (2, 15), (8, 10), (5, 8), (4, 5), (6, 4), (3, 2), (4, 12) The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only a. The three cluster centers after the first round execution. b. The three clusters after the first round execution.
Suppose that the data mining task is to cluster the following eight points (with (x, y) representing location) into three clusters: (3, 10), (2, 15), (8, 10), (5, 8), (4, 5), (6, 4), (3, 2), (4, 12) The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show only a. The three cluster centers after the first round execution. b. The three clusters after the first round execution.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Suppose that the data mining task is to cluster the following eight points (with (x, y)
representing location) into three clusters:
(3, 10), (2, 15), (8, 10), (5, 8), (4, 5), (6, 4), (3, 2), (4, 12)
The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively.
Use the k-means
a. The three cluster centers after the first round execution.
b. The three clusters after the first round execution.
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